UKDriverDeaths              package:ts              R Documentation

_R_o_a_d _C_a_s_u_a_l_t_i_e_s _i_n _G_r_e_a_t _B_r_i_t_a_i_n _1_9_6_9-_8_4

_D_e_s_c_r_i_p_t_i_o_n:

     'UKDriverDeaths' is a time series giving the monthly totals of car
     drivers in Great Britain killed or seriously injured Jan 1969 to
     Dec 1984. Compulsory wearing of seat belts was introduced on 31
     Jan 1983.

     'Seatbelts' is more information on the same problem.

_U_s_a_g_e:

     data(UKDriverDeaths)
     data(Seatbelts)

_F_o_r_m_a_t:

     'Seatbelts' is a multiple time series, with columns

     '_D_r_i_v_e_r_s_K_i_l_l_e_d' car drivers killed.

     '_d_r_i_v_e_r_s' same as 'UKDriverDeaths'.

     '_f_r_o_n_t' front-seat passengers killed or seriously injured.

     '_r_e_a_r' rear-seat passengers killed or seriously injured.

     '_k_m_s' distance driven.

     '_P_e_t_r_o_l_P_r_i_c_e' petrol price.

     '_V_a_n_K_i_l_l_e_d' number of van ('light goods vehicle') drivers.

     '_l_a_w' 0/1: was the law in effect that month?

_S_o_u_r_c_e:

     Harvey, A.C. (1989) _Forecasting, Structural Time Series Models
     and the Kalman Filter._ Cambridge University Press, pp. 519-523.

     Durbin, J. and Koopman, S. J. (2001) _Time Series Analysis by
     State Space Methods._  Oxford University Press. <URL:
     http://www.ssfpack.com/dkbook/>

_R_e_f_e_r_e_n_c_e_s:

     Harvey, A. C. and Durbin, J. (1986) The effects of seat belt
     legislation on British road casualties: A case study in structural
     time series modelling. _Journal of the Royal Statistical Society_
     series B, *149*, 187-227.

_E_x_a_m_p_l_e_s:

     data(UKDriverDeaths)
     ## work with pre-seatbelt period to identify a model, use logs
     work <- window(log10(UKDriverDeaths), end = 1982+11/12)
     par(mfrow = c(3,1))
     plot(work); acf(work); pacf(work)
     par(mfrow = c(1,1))
     (fit <- arima(work, c(1,0,0), seasonal = list(order= c(1,0,0))))
     z <- predict(fit, n.ahead = 24)
     ts.plot(log10(UKDriverDeaths), z$pred, z$pred+2*z$se, z$pred-2*z$se,
             lty = c(1,3,2,2), col = c("black", "red", "blue", "blue"))

     ## now see the effect of the explanatory variables
     data(Seatbelts)
     X <- Seatbelts[, c("kms", "PetrolPrice", "law")]
     X[, 1] <- log10(X[, 1]) - 4
     arima(log10(Seatbelts[, "drivers"]), c(1,0,0),
           seasonal = list(order= c(1,0,0)), xreg = X)

